zzp1221/Research-Assistant-Agent

This page introduces an external open-source repository. It is not an HDATF product.

What it does

Research Assistant Agent is a multi-agent research execution prototype for researchers reproducing papers, evaluating datasets and exploring candidates within a budget. Inputs are natural-language research goals, papers or attachments, repository addresses, datasets and resource constraints. A planner builds a dependency graph, meaning a plan with explicit task prerequisites, and a scheduler dispatches specialist agents with approval, retry, cancellation and budget handling. Outputs include experiment artifacts, metrics, environment manifests and links between individual claims and evidence, with separate outcomes for completed, partial, minimal-check, blocked and review-needed work. It also exposes literature search, page-based document retrieval, REST interfaces and live execution events to a React workbench. The main implementation combines a Go backend, a separate sandbox service and a Python optimizer, and the documented Compose setup requires Docker and an OpenAI-compatible model endpoint and key. Starting through that setup requires configuring service authentication and model access before opening the workbench, rather than merely opening a static page. Older root instructions say the backend is Linux-only, while the newer desktop section and desktop README describe a Windows Wails shell, native sandbox support and cross-builds, so native Windows readiness cannot be treated as settled from the overview alone. It remains a research prototype with limited production authentication and incomplete public raw verification logs. The verification document still lists claim-rubric parsing and paper-metadata extraction defects, and successful minimal runs or offline framework baselines are not full paper reproduction or real framework performance evidence.

License

MIT Permissive. Commercial use and changes are allowed if the copyright notice is kept.

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Only repositories in the ranked Trendshift lists are included, and the lists are used only to find candidates. We do not copy their ranks. Descriptions, licenses and star counts come from each GitHub repository. The notes are our own reading. We have not tested these projects, and a place on a trending list does not prove quality.